Instructions to use Kikinoking/MNLP_M3_quantized_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kikinoking/MNLP_M3_quantized_model with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Kikinoking/MNLP_M3_quantized_model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Kikinoking/MNLP_M3_quantized_model")
model = AutoModelForCausalLM.from_pretrained("Kikinoking/MNLP_M3_quantized_model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Kikinoking/MNLP_M3_quantized_model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kikinoking/MNLP_M3_quantized_model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kikinoking/MNLP_M3_quantized_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Kikinoking/MNLP_M3_quantized_model

SGLang

How to use Kikinoking/MNLP_M3_quantized_model with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Kikinoking/MNLP_M3_quantized_model" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kikinoking/MNLP_M3_quantized_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Kikinoking/MNLP_M3_quantized_model" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kikinoking/MNLP_M3_quantized_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Kikinoking/MNLP_M3_quantized_model with Docker Model Runner:
```
docker model run hf.co/Kikinoking/MNLP_M3_quantized_model
```

Kikinoking commited on Jun 10, 2025

Commit

45f7328

verified ·

1 Parent(s): 7548dee

Update README.md

Browse files

Files changed (1) hide show

README.md +27 -0

README.md CHANGED Viewed

@@ -45,3 +45,30 @@ with torch.inference_mode():
     output = model.generate(**inputs, max_new_tokens=1)
 print("Answer:", tokenizer.decode(output[0], skip_special_tokens=True))

     output = model.generate(**inputs, max_new_tokens=1)
 print("Answer:", tokenizer.decode(output[0], skip_special_tokens=True))
+##How It Was Built
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+import torch
+base_id = "aidasvenc/MNLP_M3_mcqa_model"
+qcfg = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_use_double_quant=True
+)
+tokenizer = AutoTokenizer.from_pretrained(base_id)
+model = AutoModelForCausalLM.from_pretrained(
+    base_id,
+    quantization_config=qcfg,
+    device_map="auto",
+    torch_dtype="auto"
+)
+# Push to Hugging Face Hub
+model.push_to_hub("Kikinoking/MNLP_M3_quantized_model", private=True)
+tokenizer.push_to_hub("Kikinoking/MNLP_M3_quantized_model")
+print("VRAM used (MiB):", torch.cuda.memory_reserved()/1024**2)